Eric Maris

Estimating Multiple Classification Latent Class Models.

This paper presents a new class of models for persons-by-items data. The essential new feature of this class is the representation of the persons: every person is represented by its membership tomultiple latent classes, each of which belongs to onelatent classification. The models can be considered as a formalization of the hypothesis that the responses come about in a process that involves the application of a number ofmental operations. Two algorithms for maximum likelihood (ML) and maximum a posteriori (MAP) estimation are described. They both make use of the tractability of the complete data likelihood to maximize the observed data likelihood. Properties of the MAP estimators (i.e., uniqueness and goodness-of-recovery) and the existence of asymptotic standard errors were examined in a simulation study. Then, one of these models is applied to the responses to a set of fraction addition problems. Finally, the models are compared to some related models in the literature.

Parieto-occipital sources account for the increase in alpha activity with working memory load

The role of oscillatory alpha activity (8–13 Hz) in cognitive processing remains an open question. It has been debated whether alpha activity plays a direct role in the neuronal processing required for a given task or whether it reflects idling and/or functional inhibition. Recent electroencephalography (EEG) studies have demonstrated that alpha activity increases parametrically with load during retention in working memory paradigms. While it is known that the parieto‐occipital cortex is involved in the generation of the spontaneous alpha oscillations, it remains unknown where the sources of the memory‐dependent alpha activity are located. We recorded brain activity using magnetoencephalography (MEG) from human subjects performing a Sternberg memory task where faces were used as stimuli. Spectral analysis revealed a parametric increase in alpha activity with memory load over posterior brain areas. We then applied a source reconstruction technique that allowed us to map the parametric increase in alpha activity to the anatomical magnetic resonance (MR) images of the subject. The primary sources of the memory‐dependent alpha activity were in the vicinity of the parieto‐occipital sulcus. This region is not directly involved in working memory maintenance of faces. Our findings are consistent with the notion that alpha activity reflects disengagement or inhibition of the visual dorsal stream. We propose that the disengagement reflected in alpha power serves to suppress visual input in order to devote resources to structures responsible for working memory maintenance. Hum Brain Mapp 2007.

PSYCHOMETRIC LATENT RESPONSE MODELS

In this paper, some psychometric models will be presented that belong to the larger class oflatent response models (LRMs). First, LRMs are introduced by means of an application in the field ofcomponential item response theory (Embretson, 1980, 1984). Second, a general definition of LRMs (not specific for the psychometric subclass) is given. Third, some more psychometric LRMs, and examples of how they can be applied, are presented. Fourth, a method for obtaining maximum likelihood (ML) and some maximum a posteriori (MAP) estimates of the parameters of LRMs is presented. This method is then applied to theconjunctive Rasch model. Fifth and last, an application of the conjunctive Rasch model is presented. This model was applied to responses to typical verbal ability items (open synonym items).

Probability Matrix Decomposition Models.

In this paper, we consider a class of models for two-way matrices with binary entries of 0 and 1. First, we considerBoolean matrix decomposition, conceptualize it as alatent response model (LRM) and, by making use of this conceptualization, generalize it to a larger class of matrix decomposition models. Second,probability matrix decomposition (PMD) models are introduced as a probabilistic version of this larger class of deterministic matrix decomposition models. Third, an algorithm for the computation of the maximum likelihood (ML) and the maximum a posteriori (MAP) estimates of the parameters of PMD models is presented. This algorithm is an EM-algorithm, and is a special case of a more general algorithm that can be used for the whole class of LRMs. And fourth, as an example, a PMD model is applied to data on decision making in psychiatric diagnosis.

Phonological ambiguity and context sensitivity: On sublexical clustering in visual word recognition

In one lexical decision and three naming experiments, we established the effect of visually separating two letters that have to be considered jointly for pronunciation. Segmentation effects were studied for digraphic vowels and for ambiguous onset-letter (C) whose pronunciation is determined by the following vowel. Separating the two letters of a digraphic vowel (e.g., BO//EK) impaired reading in all experiments. Separating the onset C from the letter that resolves its ambiguity (e.g., C//ENT) did not impair reading more than separating an unambiguous onset-letter from the following vowel (e.g., T//ENT). However, there was a general processing cost for items with an ambiguous onset in terms of speed and accuracy. The conclusion is that local phonological ambiguity is resolved in two different stages: One that is sensitive to visual presentation and one that is not.

Testing the race model inequality: A nonparametric approach

Abstract This paper introduces a nonparametric procedure for testing the race model explanation of the redundant signals effect. The null hypothesis is the race model inequality derived from the race model by Miller (Cognitive Psychol. 14 (1982) 247). The construction of a nonparametric test is made possible by a small change in the usual experimental procedure. This change involves that whenever only a single stimulus is presented, its modality is determined independently from the previous trials. It is shown that the test procedure is consistent against every violation of the null hypothesis. The test procedure is developed for data from a single participant, but it can easily be extended to the testing of the null hypothesis across participants, and this is also shown in the paper.

A MCMC-method for models with continuous latent responses

This paper introduces a new technique for estimating the parameters of models with continuous latent data. Using the Rasch model as an example, it is shown that existing Bayesian techniques for parameter estimation, such as the Gibbs sampler, are not always easy to implement. Then, a new sampling-based Bayesian technique, called the DA-T-Gibbs sampler, is introduced. The DA-T-Gibbs sampler relies on the particular latent data structure of latent response models to simplify the computations involved in parameter estimation.

Perceptual analysis of two-way two-mode frequency data: probability matrix decomposition and two alternatives

Abstract A perceptual mapping technique for the analysis of two-way two-mode frequency data is presented: probability matrix decomposition. The technique is compared, both theoretically and empirically, to two alternative techniques: latent class analysis by the binomial model and correspondence analysis. From a theoretical perspective the most salient difference is that probability matrix decomposition (PMD) allows for testing several decision rules, each of which constitutes a different model of the psychological process assumed to give rise to the data. This distinguishes PMD from both correspondence analysis and latent class analysis, which can be considered tools for ‘data reduction’ without any underlying theory. When PMD models adequately reflect the underlying decision process, the technique is expected to lead to a more accurate representation of the data. The empirical comparison was based on a set of judgements obtained from 50 consumers concerning the appropriateness of 11 attributes for 41 sandwich fillings. Applying each of the three techniques to the binary judgements aggregated across respondents showed that PMD had the best fit in representing the data and also had the largest predictive power with respect to the preferences of consumers for the sandwich fillings. These findings support the hypothesis that modelling the underlying process may lead to a more accurate representation. Regarding the interpretability of the resulting perceptual maps, there also was an advantage for PMD. When considering the ease of data analysis however, correspondence analysis seems to be superior.